Search Results (238)

Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity. While Graph Attention Network (GAT) frameworks are widely available, this study advances the state of the art by introducing a Bayesian GAT method specifically tailored for vibration-based compressor fault diagnosis. The approach integrates domain-specific digital-twin simulations built with Rotordynamic software (1.3.0), and constructs dual adjacency matrices to encode both physically informed and data-driven sensor relationships. Additionally, a hybrid forecasting-and-reconstruction objective enables the model to capture short-term deviations as well as long-term waveform fidelity. Monte Carlo dropout further decomposes prediction uncertainty into aleatoric and epistemic components, providing a more robust and interpretable model. Comparative evaluations against conventional Long Short-Term Memory (LSTM)-based autoencoder and forecasting methods demonstrate that the proposed framework achieves superior fault-detection performance across multiple fault types, including misalignment, bearing failure, and unbalance. Moreover, uncertainty analyses confirm that fault severity correlates with increasing levels of both aleatoric and epistemic uncertainty, reflecting heightened noise and reduced model confidence under more severe conditions. By enhancing GAT fundamentals with a domain-tailored dual-graph strategy, specialized Bayesian inference, and digital-twin data generation, this research delivers a comprehensive and interpretable solution for compressor fault diagnosis, paving the way for more reliable and risk-aware predictive maintenance in complex rotating machinery. Full article

Recent advancements in deep learning have spurred significant research interest in fault diagnosis for elevator systems. However, conventional approaches typically require substantial labeled datasets that are often impractical to obtain in real-world industrial environments. This limitation poses a fundamental challenge for developing robust diagnostic models capable of performing reliably under data-scarce conditions. To address this critical gap, we propose MetaRes-DMT-AS (Meta-ResNet with Dynamic Meta-Training and Adaptive Scheduling), a novel meta-learning framework for few-shot fault diagnosis. Our methodology employs Gramian Angular Fields to transform 1D raw sensor data into 2D image representations, followed by episodic task construction through stochastic sampling. During meta-training, the system acquires transferable prior knowledge through optimized parameter initialization, while an adaptive scheduling module dynamically configures support/query sets. Subsequent regularization via prototype networks ensures stable feature extraction. Comprehensive validation using the Case Western Reserve University bearing dataset and proprietary elevator acceleration data demonstrates the framework’s superiority: MetaRes-DMT-AS achieves state-of-the-art few-shot classification performance, surpassing benchmark models by 0.94–1.78% in overall accuracy. For critical few-shot fault categories—particularly emergency stops and severe vibrations—the method delivers significant accuracy improvements of 3–16% and 17–29%, respectively. Full article

The Mazenod Lake region of the southern Great Bear Magmatic Zone (GBMZ) of the Northwest Territories, Canada, comprises the north-central portion of the Faber volcano-plutonic belt. Widespread and abundant surface exposure of several coalescing hydrothermal systems enables this paper to document, without ambiguity, the relationships between geology, structure, alteration, and mineralization in this well exposed iron-oxide–copper–gold (IOCG) mineral system. Mazenod geology comprises rhyodacite to basaltic-andesite ignimbrite sheets with interlayered volcaniclastic sedimentary rocks dominated by fine-grained laminated tuff sequences. Much of the intermediate to mafic nature of volcanic rocks is masked by low-intensity but pervasive metasomatism. The region is affected by a series of coalescing magmatic–hydrothermal systems that host the Sue–Dianne magnetite–hematite IOCG deposit and several related showings including magnetite, skarn, and iron oxide apatite (IOA) styles of alteration ± mineralization. The mid to upper levels of these systems are exposed at surface, with underlying batholith, pluton and stocks exposed along the periphery, as well as locally within volcanic rocks associated with more intense alteration and mineralization. Widespread alteration includes potassic and sodic metasomatism, and silicification with structurally controlled giant quartz complexes. Localized tourmaline, skarn, magnetite–actinolite, and iron-oxide alteration occur within structural breccias, and where most intense formed the Sue–Dianne Cu-Ag-Au diatreme-like breccia deposit. Magmatism, volcanism, hydrothermal alteration, and mineralization formed during a negative tectonic inversion within the Wopmay Orogen. This generated a series of oblique offset rifted basins with continental style arc magmatism and extensional structures unique to GBMZ rifting. All significant hydrothermal centers in the Mazenod region occur along and at the intersections of crustal faults either unique to or put under tension during the GBMZ inversion. Full article

Large steam turbine generators are increasingly vulnerable to damage from shaft voltages and bearing currents due to the widespread adoption of modern power electronic excitation systems and more flexible operating regimes. Earthing brushes provide a critical path for discharging these shaft currents and voltages, but their effectiveness depends on the timely detection of brush degradation or faults. Conventional monitoring of shaft voltage and current is often rudimentary, typically limited to peak readings, making it challenging to identify specific fault conditions before mechanical damage occurs. This study addresses this gap by systematically analyzing shaft voltage and current signals under various controlled earthing brush fault conditions (floating brushes, worn brushes, and oil/dust contamination) in several large turbine generators. Experimental site tests identified distinct electrical signatures associated with each fault type, demonstrating that online shaft voltage and current measurements can reliably detect and classify earthing brush faults. These include unique RMS, DC, and harmonic patterns in both voltage and current signals, enabling accurate fault classification. These findings highlight the potential for more proactive maintenance and condition-based monitoring, which can reduce unplanned outages and improve the reliability and safety of power generation systems. Full article

The stability of gearbox bearings is crucial to the operational efficiency and safety of industrial equipment, as their faults can lead to downtime, economic losses, and safety risks. Traditional models face difficulties in handling complex industrial time-series data due to insufficient feature extraction capabilities and poor training stability. Although transformers show advantages in fault diagnosis, their ability to model local dependencies is limited. To improve feature extraction from time-series data and enhance model robustness, this paper proposes an innovative method based on the ViT. Time-series data were converted into two-dimensional images using polar coordinate transformation and Gramian matrices to enhance classification stability. A lightweight front-end encoder and depthwise feature extractor, combined with multi-scale depthwise separable convolution modules, were designed to enhance fine-grained features, while two-dimensional rotary position encoding preserved temporal information and captured temporal dependencies. The constructed RoPE-DWTrans model implemented a unified feature extraction process, significantly improving cross-dataset adaptability and model performance. Experimental results demonstrated that the RoPE-DWTrans model achieved excellent classification performance on the combined MCC5 and HUST gearbox datasets. In the fault category diagnosis task, classification accuracy reached 0.953, with precision at 0.959, recall at 0.973, and an F1 score of 0.961; in the fault category and severity diagnosis task, classification accuracy reached 0.923, with precision at 0.932, recall at 0.928, and an F1 score of 0.928. Compared with existing methods, the proposed model showed significant advantages in robustness and generalization ability, validating its effectiveness and application potential in industrial fault diagnosis. Full article

The Aghbar-Bou Azzer East mining district (ABED) is located between the Bou Azzer East and Aghbar deposits. It is an area of approximately 7 km long towards ENE–WSW and 2 km wide towards N–S. In this barren area, volcano-sedimentary rocks are attributed to the Ouarzazate group outcrop (Ediacarian age): they are composed of volcanic rocks (ignimbrite, andesite, rhyolite, dacite, etc.) covered by the Adoudou detritic formation in angular unconformity. Given the absence of serpentinite outcrops, exploration investigation in this area has been very limited. This paper aims to use ionic leach geochemistry (on samples of soil) to detect the presence of Co-bearing arsenides above hidden ore deposits in this unexplored area of the Bou Azzer inlier. In addition, a detailed structural analysis allowed the identification of four families of faults and fractures with or without filling. Three directional major fault systems of several kilometers in length and variable orientation in both the Cryogenian basement and the Ediacaran cover have been identified: (i) ENE–WSW, (ii) NE–SW, and (iii) NW–SE. Several geochemical anomalies for Co, As, Ni, Ag, and Cu are aligned along three main directions, including NE–SW, NW–SE, and ENE–WSW. They are particularly well-defined in the western zone but are only minor in the central and eastern zones. Some of these anomalies correlate with the primary structural features observed in the studied area. These trends are consistent with those known under mining exploitation in nearby ore deposits, supporting the potential for similar mineralization in the ABED. Based on structural analysis and ionic leach geochemistry, drilling programs were conducted in the study area, confirming the continuity of serpentinites at depth beneath the Ediacaran cover and the presence of Co–Fe-bearing arsenide ores. This validates the ionic geochemistry technique as a reliable method for exploring buried ore deposits. Full article

Reliable fault diagnosis in aero-engine bearing systems is essential for maintaining process stability and safety. However, acquiring fault samples in aerospace applications is costly and difficult, resulting in severely limited data for model training. Traditional methods often perform poorly under such constraints, lacking the ability to extract discriminative features or effectively correlate observed signal changes with underlying process faults. To address this challenge, this study presents a process-oriented framework—WSET-CNN-OOA-LSSVM—designed for effective fault recognition in small-sample scenarios. The framework begins with Wavelet Synchroextracting Transform (WSET), enhancing time–frequency resolution and capturing energy-concentrated fault signatures that reflect degradation along the process timeline. A tailored CNN with asymmetric pooling and progressive dropout preserves temporal dynamics while preventing overfitting. To compensate for limited labels, confidence-based pseudo-labeling is employed, guided by Mahalanobis distance and adaptive thresholds to ensure reliability. Classification is finalized using an Osprey Optimization Algorithm (OOA)-enhanced Least Squares SVM, which adapts decision boundaries to reflect subtle process state transitions. Validated on both test bench and real aero-engine data, the framework achieves 93.4% accuracy with only five fault samples per class and 100% in full-scale scenarios, outperforming eight existing methods. Therefore, the experimental results confirm that the proposed framework can effectively overcome the data scarcity challenge in aerospace bearing fault diagnosis, demonstrating its practical viability for few-shot learning applications in industrial condition monitoring. Full article

Bearings are vital apparatuses in many industrial systems, and their failure can lead to severe damage, costly downtime, and safety risks. Therefore, early detection of bearing faults is critical to prevent catastrophic failures. However, diagnosing bearing faults in real-world conditions is challenging due to noise, which can obscure vibration signals and reduce the effectiveness of traditional diagnostic techniques. This paper portrays a unique method for bearing fault identification in high-noise environments by integrating Improved Complete Ensemble Empirical Mode Decomposition (ICEEMD) and Continuous Wavelet Transform (CWT). ICEEMD decomposes complex vibration signals into intrinsic mode functions, effectively filtering out noise and enhancing feature extraction. CWT is then applied to obtain a time–frequency representation of the cleaned signal, allowing for precise detection of transient events and frequency variations associated with faults. The proposed approach is evaluated using simulated signals, achieving a testing accuracy of 78% at −20 dB SNR, demonstrating its robustness in noisy environments. This study highlights the capability of combining ICEEMD and CWT for robust fault diagnosis in noisy industrial applications, paving the way for improved predictive maintenance strategies. Full article

Accurate extraction of weak fault information from non-stationary vibration signals collected by vibration sensors is challenging due to severe noise and interference. While variational mode decomposition (VMD) has been effective in fault diagnosis, its reliance on predefined parameters, such as center frequencies and mode number, limits its adaptability and performance across different signal characteristics. To address these limitations, this paper proposes an improved variational mode decomposition (IVMD) method that enhances diagnostic performance by adaptively determining key parameters based on scale space representation. In concrete, the approach constructs a scale space by computing the inner product between the signal’s Fourier spectrum and a Gaussian function, and then identifies both the mode number and initial center frequencies through peak detection, ensuring more accurate and stable decomposition. Moreover, a multipoint kurtosis (MKurt) criterion is further employed to identify fault-relevant components, which are then merged to suppress redundancy and enhance diagnostic clarity. Experimental validation on locomotive bearings with inner race faults and compound faults demonstrates that IVMD outperforms conventional VMD by effectively extracting fault features obscured by noise. The results confirm the robustness and adaptability of IVMD, making it a promising tool for fault diagnosis in complex industrial environments. Full article

In rolling bearing fault diagnosis, when an unknown fault is present, the Closed-Set Recognition (CSR) method tends to misclassify it as a known fault. To address this issue, an Open-Set Recognition (OSR) framework is proposed for rolling bearing fault diagnosis in this study. The framework is built upon a serial multi-scale convolutional prototype learning (SMCPL) network, enhanced with an efficient channel attention (ECA) mechanism to extract the most critical fault features. The extracted features are fed into the Density Peak Clustering (DPC) module, which identifies known and unknown classes based on the computed local densities and relative distances. Finally, validation is performed on the Case Western Reserve University (CWRU) dataset, the Xi’an Jiaotong University rolling bearing accelerated life test dataset (XJTU-SY), and the Paderborn University bearing dataset (PU), Germany, and the framework is comprehensively evaluated in terms of several evaluation metrics, such as normalization accuracy and feature visualization. The experimental results show that SMCPL-ECA-DPC outperforms the comparative methods of SMCPL, CPL, ANEDL, CNN, and OpenMax and has high diagnostic performance in the identification of unknown faults. Full article

The Hammam–Zriba F–Ba (Zn–Pb) stratabound deposit is located within the Zaghouan Fluorite Province (ZFP), which is the most important mineral sub-province in NE Tunisia, with several CaF₂ deposits occurring mainly along the Zaghouan Fault and corresponding to an F-rich MVT mineral system developed along the unconformity surface between the uppermost Jurassic limestones and the late Cretaceous layers. Petrographic analysis, microthermometry, and Raman spectroscopy applied to fluid inclusions in fluorite revealed various types of inclusions containing brines, oil, CO₂, and CH₄ along with solid phases such as evenkite, graphite, kerogen and bitumen. Microthermometric data indicate homogenization temperatures ranging from 85 °C to 145 ± 5 °C and salinities of 13–22 wt.% NaCl equivalent. This study supports a model of heterogeneous trapping, where saline basinal brines, oil, and gases were simultaneously trapped within fluorite, which indicates fluid immiscibility. The Raman analysis identified previously undetected organic compounds, including the first documented occurrence of evenkite, a mineral hydrocarbon, co-genetically trapped with graphite. The identification of evenkite and graphite in fluid inclusions offers new insights into the composition of hydrocarbon-bearing fluids within the MVT deposits in Tunisia, contributing to an understanding of the mineralogical characteristics of these deposits. The identified hydrocarbons correspond to three oil families. Family I (aliphatic compounds) is attributed to the lower-Eocene Bou-Dabbous Formation, family II (aromatic compounds) is attributed to the Albian Fahdene Formation and the Cenomanian–Turonian Bahloul Formation, and family III is considered as a mixture of aliphatic and aromatic compounds generated by the three sources. The presence of graphite in fluid inclusions could suggest the involvement of a thermal effect from deep-seated sources through the reservoir to the site of fluorite precipitation. These findings suggest that the fluorite mineral system might have been linked with the interaction of multi-reservoir fluids, potentially linked to the neighboring petroleum system in northeastern Tunisia during the Miocene. This study aims to investigate the composition of fluid inclusions in fluorite from the Hammam–Zriba F–Ba (Zn–Pb) deposit, with a particular focus on the plausible sources of hydrocarbons and their implications for the genetic relationship between the mineralizing system and petroleum reservoirs. Full article

The Afghanistan earthquake of 21 June 2022 ruptured a ~10 km-long fault segment in the North Waziristan–Bannu fault system (NWBFS) located towards the north of the Katawaz Basin. The earthquake was shallow and reportedly caused widespread devastation. In this article, we investigated the long-term, i.e., geological and geomorphological, evidence of deformation along the earthquake segment. For comparison, we also studied the short-term space geodetic and remote sensing results documenting a visible offset between the fault traces. Focusing on the fault modelling and on the published results, it is thus clear that the earthquake rupture did not reach the surface; instead, it stopped in the shallow sub-surface at ~1 km depth. Moreover, the InSAR analyses show some technical issues, such as coherence loss, etc., likely due to severe ground-shaking leaving some gaps in the results; geological and geomorphological evidence complemented this information. As an outcome of this research, we confirmed that InSAR results could generally capture the overall fault geometry at depth, even in cases of blind faulting, whereas the detailed geometry of the tectonic structure, in this case with a right stepping en-echelon pattern, could be successfully captured by combining it with geological and geomorphological approaches and optical remote sensing observations. Accordingly, the right stepping fault generates a restraining bend in the dominantly left-lateral shear zone. Therefore, such fault stepovers are capable of localizing strain and could act as loci for seismic ruptures, bearing strong implications for the seismic hazard assessment of the region, as well as of other strike-slip fault zones. Full article

Turbine generators are essential for power generation, but the presence of shaft voltages and currents poses significant challenges to their reliability, efficiency, and operational lifespan. These phenomena, arising from electromagnetic induction, poor shaft grounding, rotor excitation systems, and varying operational conditions, can lead to severe damage to bearings and rotors, resulting in costly downtime and maintenance. This study reviews the mechanisms behind shaft voltage and current generation, their impact on turbine generators, and the effectiveness of various mitigation strategies, including shaft earthing brushes, bearing insulation, and advanced health monitoring systems. Furthermore, it explores emerging techniques for measuring and diagnosing shaft voltage and current, as well as advancements in predictive maintenance and condition monitoring. This study further explores the integration of artificial intelligence and machine learning in predictive maintenance, leveraging real-time condition monitoring and fault diagnostics. By analyzing existing and emerging mitigation strategies, this study provides a comprehensive evaluation of solutions aimed at minimizing these electrical effects. The findings underscore the importance of proactive management strategies to enhance generator reliability, optimize maintenance practices, and improve overall power system stability. This research serves as a foundation for future advancements in shaft voltage mitigation, contributing to the long-term sustainability of power generation infrastructure. Full article

Due to the harsh and complex operating conditions, rolling element bearings (REBs) are prone to failures, which can result in significant economic losses and catastrophic breakdowns. To efficiently extract weak fault features from raw signals, singular value decomposition (SVD)-based signal denoising methods have been widely adopted in the field of rolling bearing fault diagnosis. In traditional SVD-based methods, singular components (SCs) with significant singular values are selected to reconstruct the denoized signal. However, this approach often overlooks low-energy SCs that contain important fault information, leading to inaccurate diagnosis. To address this issue, we propose a new selection scheme based on frequency domain multipoint kurtosis (FDMK), along with a reweighting strategy based on FDMK to further emphasize weak fault features. In addition, the estimation process of fault characteristic frequency is introduced, allowing FDMK to be calculated without prior information. The proposed FDMK-SVD can adaptively extract periodic fault features and accurately identify the health condition of REBs. The effectiveness of FDMK-SVD is validated using both simulated and experimental data obtained from a locomotive bearing test rig. The results show that FDMK-SVD can effectively extract fault features from raw vibration signals, even in the presence of severe background noise and other types of interferences. Full article

Accurate fault diagnosis remains a critical but unresolved issue in predictive maintenance, as industrial environments typically involve large amounts of electromagnetic interference and mechanical noise that can severely degrade the signal quality. In this study, we propose an innovative diagnostic framework to address the challenging problem of bearing fault diagnosis in vibration signals under complex noise conditions. We develop the VMD-CNN-BiLSTM-CBAM model by systematically integrating the variational mode decomposition (VMD), convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and convolutional block attention module (CBAM). The framework starts with VMD-based signal decomposition, which effectively separates the noise component from the bearing vibration features. Based on this denoising, a CNN architecture is employed to extract multi-scale spatio-temporal features through its hierarchical learning mechanism. The subsequent BiLSTM layer captures bidirectional temporal dependencies to model fault-evolution patterns, while the CBAM module strategically highlights key diagnostic features through adaptive channel spatial attention. Experimental validation using the Case Western Reserve University and Jiangnan University bearing datasets demonstrates the excellent performance of the model, with average accuracies of 99.76% and 99.40%, respectively. Finally, additional validation through our customized testbed confirms the usefulness of the model with an average accuracy of 99.70%. These results demonstrate that the proposed approach greatly improves fault diagnosis in noisy industrial environments through its synergistic architectural design and enhanced noise immunity. Full article